Clinical Decision Support Functions and Digitalization of Clinical Documents of Electronic Medical Record Systems

Objectives The objective of this study was to investigate the clinical decision support (CDS) functions and digitalization of clinical documents of Electronic Medical Record (EMR) systems in Korea. This exploratory study was conducted focusing on current status of EMR systems. Methods This study used a nationwide survey on EMR systems conducted from July 25, 2018 to September 30, 2018 in Korea. The unit of analysis was hospitals. Respondents of the survey were mainly medical recorders or staff members in departments of health insurance claims or information technology. This study analyzed data acquired from 132 hospitals that participated in the survey. Results This study found that approximately 80% of clinical documents were digitalized in both general and small hospitals. The percentages of general and small hospitals with 100% paperless medical charts were 33.7% and 38.2%, respectively. The EMR systems of general hospitals are more likely to have CDS functions of warnings regarding drug dosage, reminders of clinical schedules, and clinical guidelines compared to those of small hospitals; this difference was statistically significant. For the lists of digitalized clinical documents, almost 93% of EMR systems in general hospitals have the inpatient progress note, operation records, and discharge summary notes digitalized. Conclusions EMRs are becoming increasingly important. This study found that the functions and digital documentation of EMR systems still have a large gap, which should be improved and made more sophisticated. We hope that the results of this study will contribute to the development of more sophisticated EMR systems

Mutations in DDX58, which Encodes RIG-I, Cause Atypical Singleton-Merten Syndrome

Singleton-Merten syndrome (SMS) is an autosomal-dominant multi-system disorder characterized by dental dysplasia, aortic calcification, skeletal abnormalities, glaucoma, psoriasis, and other conditions. Despite an apparent autosomal-dominant pattern of inheritance, the genetic background of SMS and information about its phenotypic heterogeneity remain unknown. Recently, we found a family affected by glaucoma, aortic calcification, and skeletal abnormalities. Unlike subjects with classic SMS, affected individuals showed normal dentition, suggesting atypical SMS. To identify genetic causes of the disease, we performed exome sequencing in this family and identified a variant (c.1118A>C [p.Glu373Ala]) of DDX58, whose protein product is also known as RIG-I. Further analysis of DDX58 in 100 individuals with congenital glaucoma identified another variant (c.803G>T [p.Cys268Phe]) in a family who harbored neither dental anomalies nor aortic calcification but who suffered from glaucoma and skeletal abnormalities. Cys268 and Glu373 residues of DDX58 belong to ATP-binding motifs I and II, respectively, and these residues are predicted to be located closer to the ADP and RNA molecules than other nonpathogenic missense variants by protein structure analysis. Functional assays revealed that DDX58 alterations confer constitutive activation and thus lead to increased interferon (IFN) activity and IFN-stimulated gene expression. In addition, when we transduced primary human trabecular meshwork cells with c.803G>T (p.Cys268Phe) and c.1118A>C (p.Glu373Ala) mutants, cytopathic effects and a significant decrease in cell number were observed. Taken together, our results demonstrate that DDX58 mutations cause atypical SMS manifesting with variable expression of glaucoma, aortic calcification, and skeletal abnormalities without dental anomalies

Mutations in DDX58, which Encodes RIG-I, Cause Atypical Singleton-Merten Syndrome

Singleton-Merten syndrome (SMS) is an autosomal-dominant multi-system disorder characterized by dental dysplasia, aortic calcification, skeletal abnormalities, glaucoma, psoriasis, and other conditions. Despite an apparent autosomal-dominant pattern of inheritance, the genetic background of SMS and information about its phenotypic heterogeneity remain unknown. Recently, we found a family affected by glaucoma, aortic calcification, and skeletal abnormalities. Unlike subjects with classic SMS, affected individuals showed normal dentition, suggesting atypical SMS. To identify genetic causes of the disease, we performed exome sequencing in this family and identified a variant (c.1118A>C [p.GLu373Ala]) of DDX58, whose protein product is also known as RIG-I. Further analysis of DDX58 in 100 individuals with congenital glaucoma identified another variant (c.803G>T [p.Cys268Phe]) in a family who harbored neither dental anomalies nor aortic calcification but who suffered from glaucoma and skeletal abnormalities. Cys268 and Glu373 residues of DDX58 belong to ATP-binding motifs I and II, respectively, and these residues are predicted to be located closer to the ADP and RNA molecules than other nonpathogenic missense variants by protein structure analysis. Functional assays revealed that DDX58 alterations confer constitutive activation and thus lead to increased interferon (IFN) activity and IFN-stimulated gene expression. In addition, when we transduced primary human trabecular meshwork cells with c.803G>T (p.Cys268Phe) and c.1118A>C (p.Glu373A1a) mutants, cytopathic effects and a significant decrease in cell number were observed. Taken together, our results demonstrate that DDX58 mutations cause atypical SMS manifesting with variable expression of glaucoma, aortic calcification, and skeletal abnormalities without dental anomalies.X116452Ysciescopu

Abstract

Fast indexing in time sequence databases for similarity searching has attracted a lot of research recently. Most of the proposals, however, typically centered around the Euclidean distance and its derivatives. We examine the problem of multi-modal similarity search in which users can choose the best one from multiple similarity models for their needs. In this paper, we present anovel and fast indexing scheme for time sequences, when the distance function is any of arbitrary Lp norms (p =1; 2;:::;1). One feature of the proposed method is that only one index structure is needed for all Lp norms including the popular Euclidean distance (L2 norm). Our scheme achieves signi cant speedups over the state of the art: extensive experiments on real and synthetic time sequences show that the proposed method is up to 10 times faster than the best competitor.

Fast Time Sequence Indexing for Arbitrary L p Norms

Fast indexing in time sequence databases for similarity searching has attracted a lot of research recently. Most of the proposals, however, typically centered around the Euclidean distance and its derivatives. We examine the problem of multi-modal similarity search in which users can choose the best one from multiple similarity models for their needs. In this paper, we present a novel and fast indexing scheme for time sequences, when the distance function is any of arbitrary L p norms (p = 1; 2; : : : ; 1). One feature of the proposed method is that only one index structure is needed for all L p norms including the popular Euclidean distance (L 2 norm). Our scheme achieves significant speedups over the state of the art: extensive experiments on real and synthetic time sequences show that the proposed method is up to 10 times faster than the best competitor. 1 Introduction Time sequences of real-values arise in many applications such as stock market, medicine/science, and mul..

Fast Time Sequence Indexing for Arbitrary Lp Norms

Fast indexing in time sequence databases for similarity searching has attracted a lot of research recently. Most of the proposals, however, typically centered around the Euclidean distance and its derivatives. We examine the problem of multimodal similarity search in which users can choose the best one from multiple similarity models for their needs

POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004

Two classifiers-- Support Vector Machine (SVM) and Conditional Random Fields (CRFs) are applied here for the recognition of biomedical named entities. According to their different characteristics, the results of two classifiers are merged to achieve better performance. We propose an automatic corpus expansion method for SVM and CRF to overcome the shortage of the annotated training data. In addition, we incorporate a keyword-based post-processing step to deal with the remaining problems such as assigning an appropriate named entity tag to the word/phrase containing parentheses.